Combined koetter-vardy and chase decoding of cyclic codes

ABSTRACT

An apparatus having a first circuit and a second circuit is disclosed. The first circuit may be configured to generate (i) a plurality of symbols and (ii) a plurality of decision values both in response to detecting an encoded codeword. The second circuit may be configured to (i) generate a plurality of probabilities to flip one or more of the symbols based on the decision values, (ii) generate a modified probability by merging two or more of the probabilities of an unreliable position in the symbols and (iii) generate a decoded codeword by decoding the symbols using an algebraic soft-decision technique in response to the modified probability.

FIELD OF THE INVENTION

The present invention relates to decoding cyclic codes generally and,more particularly, to a method and/or apparatus for implementing acombined Koetter-Vardy and Chase decoding of cyclic codes.

BACKGROUND OF THE INVENTION

Efficient list decoding beyond half a minimum distance for Reed-Solomonand Bose and Ray-Chaudhuri (i.e., BCH) codes were first devised in 1997and later improved almost three decades after the inauguration of anefficient hard-decision decoding method. In particular, for a givenReed-Solomon code C(n,k,d), a Guruswami-Sudan decoding method correctsup to n (1−√{square root over (1−d/n)}) errors, which effectivelyachieves a Johnson bound, a general lower bound on the number of errorsto be corrected under a polynomial time for any code. Koetter and Vardyshowed a way to translate soft-decision reliability information providedby a channel into a multiplicity matrix that is directly involved in theGuruswami-Sudan method. The resulting method substantially outperformsthe Guruswami-Sudan method. Koetter et al. introduced a computationaltechnique, based upon re-encoding and coordinate transformation, thatreduces the complexity of a bivariate interpolation procedure. Justesenderived a condition for successful decoding using the Koetter-Vardymethod for soft-decision decoding by introducing a few assumptions. Leeand O'Sullivan devised an algebraic soft-decision decoder for Hermitiancodes. The algebraic soft-decision decoder follows a path set out byKoetter and Vardy for Reed-Solomon codes while constructing a set ofgenerators of a certain module from which a Q-polynomial is extractedusing the Gröbner conversion method.

Augot and Couvreur extended the Guruswami-Sudan method to achieve q-aryJohnson bounds,

${\frac{q - 1}{q}{n\left( {1 - \sqrt{1 - {\frac{q}{q - 1}\frac{d}{n}}}} \right)}},$

for subfield subcodes of Reed-Solomon codes by distributingmultiplicities across an alphabet of the q-ary subfield. However, theauthors give only an asymptotic proof and fail to provide explicitly theminimum multiplicities to achieve the Johnson bound. Wu presented a newlist decoding method (i.e., Wu method) for Reed-Solomon and binary BCHcodes. The Wu method casts the list decoding as a rational curve fittingproblem utilizing the polynomials constructed by the Berlekamp-Masseymethod. The Wu method achieves the Johnson bound for both Reed-Solomonand binary BCH codes. Beelen showed that the Wu method can be modifiedto achieve the binary Johnson bound for binary Goppa codes. We alsoshowed that when a part of the positions are pre-corrected, the Wumethod “neglects” the corrected positions and subsequently exhibits alarger list error correction capability (i.e., LECC) with smallereffective code length. A scenario of partial pre-correction is duringthe iterative decoding of product codes, where each row (column)component word is partially corrected by the preceding column (row)decoding, herein miscorrection is ignored. Pyndiah demonstrated that theiterative decoding of product codes achieves a near Shannon limit.

It would be desirable to implement a combined Koetter-Vardy and Chasedecoding of cyclic codes.

SUMMARY OF THE INVENTION

The present invention concerns an apparatus having a first circuit and asecond circuit. The first circuit may be configured to generate (i) aplurality of symbols and (ii) a plurality of decision values both inresponse to detecting an encoded codeword. The second circuit may beconfigured to (i) generate a plurality of probabilities to flip one ormore of the symbols based on the decision values, (ii) generate amodified probability by merging two or more of the probabilities of anunreliable position in the symbols and (iii) generate a decoded codewordby decoding the symbols using an algebraic soft-decision technique inresponse to the modified probability.

The objects, features and advantages of the present invention includeproviding a combined Koetter-Vardy and Chase decoding of cyclic codesthat may (i) merge two or more most reliable flipping estimations of anunreliable position into a single estimation, (ii) add two probabilitiesprior to base multiplicity assignments, (iii) combine Chase decodingwith Koetter-Vardy decoding, (iv) decode BCH codewords and/or (v) decodeReed-Solomon codewords.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will be apparent from the following detailed description andthe appended claims and drawings in which:

FIG. 1 is a block diagram of a communication system;

FIG. 2 is a block diagram of an example implementation of a soft decodercircuit in accordance with a preferred embodiment of the presentinvention;

FIG. 3 is a detailed block diagram of an example implementation of analgebraic soft-decision decoder circuit;

FIG. 4 is a diagram of an example merging of probability values;

FIG. 5 is a graph of a multiplicity ratio of the Guruswami-Sudantechnique over a Wu technique;

FIG. 6 is a graph of a cost ratio of the Guruswami-Sudan technique overthe Wu technique for decoding binary BCH/Goppa codes up to the binaryJohnson bound;

FIG. 7 is a graph of ratios of q-ary list decoding capabilities tominimum distances as a function of ratio of minimum distances to theeffective code length;

FIG. 8 is a graph of simulation performance comparisons for decoding a(458, 410) Reed-Solomon code under a BPSK modulation; and

FIG. 9 is a graph os simulation performance comparisons for decoding a(255, 191) Reed-Solomon code under a QAM modulation.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Some embodiments of the present invention generally enhance bounds of aGuruswami-Sudan technique (or method or process) by converting apresumed number of list correctable errors into a posterioriprobabilities. A minimum multiplicity assignment (in real numbers) forthe Guruswami-Sudan technique may be utilized to achieve a q-ary Johnsonbound,

${\frac{q - 1}{q}{n\left( {1 - \sqrt{1 - {\frac{q}{q - 1}\frac{d}{n}}}} \right)}},$

where n and d generally denote the code length and the designed minimumdistance, respectively. A fraction of positions may be pre-corrected.Subsequently, with appropriate multiplicity assignments, the a decodermay treat the code as a shortened code by truncating the pre-correctedpositions. In particular, if n* is a remaining number of uncertainpositions, the resulting list decoding radius achieves a shortenedlength q-ary Johnson bound,

${\frac{q - 1}{q}{n^{*}\left( {1 - \sqrt{1 - {\frac{q}{q - 1}\frac{d}{n^{*}}}}} \right)}},$

which may converge to the minimum distance d as an effective code lengthn* is reduced toward to the (nontrivial) q-ary minimum code length

$\frac{q}{q - 1}{d.}$

Some multiplicities (in real numbers) may achieve any list errorcorrection capability (e.g., LECC) within the q-ary Johnson bound. TheLECC bound may be enhanced by incorporating a modulation method,including binary phase-shift keying (e.g., BPSK) and quadratureamplitude modulation (e.g., QAM) modulations. The derivative bounds aregenerally beyond the q-ary Johnson bound. A combination of Chaseflipping and Koetter-Vardy varying multiplicities may also be utilized.

Referring to FIG. 1, a block diagram of a communication system (orapparatus) 90 is shown. The system 90 generally comprises a block (orcircuit) 92, a block (or circuit) 94 and a block (or circuit) 100. Thecircuit 100 generally comprises a block (or circuit) 102 and a block (orcircuit) 104. The circuits 92 to 104 may represent modules and/or blocksthat may be implemented as hardware, software, a combination of hardwareand software, or other implementations.

A signal (e.g., IN) may be received by the circuit 92. The signal IN mayimplement an input signal carrying codewords (or words or symbols orbits) to be encoded and transferred/stored. The circuit 92 may generatea signal (e.g., TX) received by the circuit 94. The signal TX mayimplement a transmit signal that conveys the encoded codewords from thesignal IN. A signal (e.g., RX) may be generated by the circuit 94 andreceived by the circuit 100/102. The signal RX may implement a receivedsignal. In the absence of errors, the codewords in the signal RX maymatch the codewords in the signal TX. The circuit 102 may generate asignal (e.g., WORD) received by the circuit 104. The signal WORD maycarry a received codeword (or bits or symbols) detected in the signalRX. A signal (e.g., DEC) may also be generated by the circuit 102 andreceived by the circuit 104. The signal DEC may convey decisions aboutthe received codewords in the signal RX. A signal (e.g., OUT) may begenerated by the circuit 104/100. The signal OUT may implement an outputsignal that contains the decoded codeword (or word or symbols or bits).

The circuit 92 may implement an encoder circuit. The circuit 92 isgenerally operation to encode the codewords received in the signal IN.The encoding may be a cyclic code encoding, a Bose and Ray-Chaudhuri(e.g., BCH) encoding or a Reed-Solomon encoding. The encoded codewordsmay be presented in the signal TX.

The circuit 94 may implement a communication channel. The circuit 94 isgenerally operational to carry the encoded codewords communicated fromthe circuit 92 to the circuit 100. The circuit 94 may also carry datacommunicated from the circuit 100 to the circuit 92. Implementations ofthe circuit 94 may include, but are not limited to, one or moretransmission medium such as air, wire, optical fibre, Ethernet and thelike. In some embodiments of the present invention, the circuit 94 mayimplement a storage medium. Storage media may include, but is notlimited to, optical media, magnetic media and electronic media.

The circuit 100 may implement a receiver circuit. The circuit 100 isgenerally operational to decode the encoded codewords received in thesignal RX. The decoding may include a soft detection and a soft decode.The received and decoded codewords may be presented in the signal OUT.

The circuit 102 may implement a soft detector circuit. The circuit 102is generally operational to detect the codewords received in the signalRX using a soft decision and/or a hard decision. The detected codewordsmay be presented in the signal WORD. Decision values corresponding tothe detected codewords may be presented in the signal DEC.

The circuit 104 may implement a soft decoder circuit. The circuit 104 isgenerally operational to decode the codewords received in the signalWORD based on the decision values received in the signal DEC. The softdecoding may include, but is not limited to, (i) generating a pluralityof probabilities to flip one or more of the symbols within the codewordsbased on said decision values, (ii) generating a modified probability bymerging two or more of the probabilities of an unreliable position inthe symbols and (iii) generating the decoded codeword by decoding thesymbols using an algebraic soft-decision technique in response to themodified probability. In some embodiments, the probabilities may begenerated using a Chase technique. The algebraic soft-decision techniquegenerally comprises a Koetter-Vardy technique. The encoded codewords maycomprise Reed-Solomon codewords or BCH codewords.

Referring to FIG. 2, a block diagram of an example implementation of thecircuit 104 is shown in accordance with a preferred embodiment of thepresent invention. The circuit 104 generally comprises a block (orcircuit) 106 and a block (or circuit) 108. The circuits 106 to 108 mayrepresent modules and/or blocks that may be implemented as hardware,software, a combination of hardware and software, or otherimplementations.

The signal DEC may be received by the circuit 106. The signal WORD maybe received by the circuit 108. A signal (e.g., PROB) may be generatedby the circuit 106 and presented to the circuit 108. The signal PROBgenerally conveys probability values for flipping bits or symbols in thereceived codewords in the signal WORD. A signal (e.g., RE) may begenerated by the circuit 108 and received by the circuit 106. The signalRE may convey re-encoded data generated during the decoding of thecodewords in the signal WORD. The signal OUT may be generated by thecircuit 108.

The circuit 106 may implement a merge circuit. The circuit 106 isgenerally operational to generate the probabilities to flip one or moreof the symbols/bits in the codewords based on the decision values in thesignal DEC. The circuit 106 may also be operational to generate amodified probability by merging two or more of the probabilities of anunreliable position in the symbols/bits. In some embodiments, themerging may merge Chase reliabilities created by the Chase technique.The Chase technique is generally described in the paper, “A class ofalgorithms for decoding block codes with channel measurementinformation”, IEEE Trans. Inform. Theory, vol. 18, pp. 170-182, January1972, which is hereby incorporated by reference in its entirety. Themerged reliabilities may be presented as a merged matrix in the signalPROB.

The circuit 108 may implement an algebraic soft-decision decodercircuit. The circuit 108 is generally operational to generate thedecoded codewords by decoding the symbols/bits using an algebraicsoft-decision technique in response to the modified probability. In someembodiments, the algebraic soft-decision technique may be theKoetter-Vardy technique. The Koetter-Vardy technique is generallydescribed in the paper, “Algebraic soft-decision decoding ofReed-Solomon codes”, IEEE Trans. Inform. Theory, vol. 49, no. 11, pp.2809-2825, November 2003, which is hereby incorporated by reference inits entirety.

Referring to FIG. 3, a detailed block diagram of an exampleimplementation of the circuit 108 is shown. The circuit 108 generallycomprises a block (or circuit) 110, a block (or circuit) 112, a block(or circuit) 114, a block (or circuit) 116, a block (or circuit) 118, ablock (or circuit) 120, a block (or circuit) 122, a block (or circuit)124, a block (or circuit) 126 and a block (or circuit) 128. The circuits110 to 128 may represent modules and/or blocks that may be implementedas hardware, software, a combination of hardware and software, or otherimplementations.

The signal PROB may be received by the circuit 112. The signal WORD maybe received by the circuits 110 and 120. The signal RE may be generatedby the circuit 120 and transferred to the circuits 106, 110 and 126. Thesignal OUT may be generated by the circuit 128.

The circuit 110 may implement an adder circuit. The circuit 110 may beoperational to sum the received word in the signal WORD with are-encoded codeword received from the circuit 120 in the signal RE. Thesum generally results in a word where the k most reliable positions arezeros.

The circuit 112 may implement a base multiplicity assignment circuit.The circuit 112 may be operational to compute a multiplicity matrixbased on the merged reliability matrix received in the signal PROB. Themultiplicity matrix may be presented to the circuit 114.

The circuit 114 may implement an interpolation circuit. The circuit 114may be operational to interpolate the constant term of the bivariatepolynomial Q(x,y) based on the multiplicity matrix computed by thecircuit 112. The result may be transferred to the circuit 116.

The circuit 116 may implement a checking circuit. The circuit 116 may beoperational to check if a constant term Q(0,0) is zero. If not, thecorresponding bivariate polynomial Q(x,y) does not yield a validcodeword and thus is invalid. Therefore, Chase flipping may be performedin the circuit 118 in an attempt to correct the errors. If the constantterm is zero, the bivariate polynomial Q(x,y) may be calculated in thecircuit 122.

The circuit 118 may implement a Chase flipping circuit. The circuit 118may be operational to flip a symbol based on the provided candidateflipping patterns. After the symbols has been flipped, the interpolationmay be performed again in the circuit 114.

The circuit 120 may implement a re-encoder circuit. The circuit 120 isgenerally operational to erase the n−k least reliable positions in thecodeword received in the signal WORD. The remaining k most reliablepositions may be re-encoded into a valid codeword. The re-encodedcodeword may be presented in the signal RE to the circuits 106, 110 and126.

The circuit 122 may implement an interpolation circuit. The circuit 122is generally operational to fully compute the bivariate polynomialQ(x,y). The computation may utilize the information that the constantterm is zero.

The circuit 124 may implement a factorize circuit. The circuit 124 isgenerally operational to factorize the polynomial to retrieve the validcodeword whose n−k least reliable positions are to be determined. Thefactorized polynomial may be presented to the circuit 126.

The circuit 126 may implement an adder circuit. The circuit 126 isgenerally operational to add the re-encoded codeword to the factorizedcodeword. The sum generally forms a partially corrected candidate wordover the received word.

The circuit 128 may implement and erasure decoder circuit. The circuit128 is generally operational to apply an erasure-only decoding tocorrect any errors that may remain in the n−k positions of the wordreceived from the circuit 126.

Referring to FIG. 4, a diagram of an example merging of probabilityvalues is shown. The merging operation may be performed by the circuit106. Each position (e.g., Yi) of the codeword received through thechannel may have corresponding hard-decision value (e.g., u_(i)), asecondary channel decision value (e.g., u′_(i)) and channel measurementinformation (e.g., α₁-₄), see reference arrow 140. The decision valuesand channel information may correspond to a posteriori probabilityvalues (e.g., π_((i,ui)) and π_((i,αi))), respectively. Each secondarydecision value may correspond to an a posteriori probability value(e.g., π_((i,u′i))). The circuit 106 may merge the two probabilityvalues for the hard-decision values and the secondary decision values(e.g., π_((i,u′i))+π_((i,ui))) in the unreliable positions, seereference arrow 142. Thus the hard-decision probabilities for positionYi may become a summed probability.

The real-number multiplicity assignment generally enables theGuruswami-Sudan technique to achieve q-ary Johnson bounds for q-ary BCHcodes. The multiplicity assignment may be tightened to achieve any LECCvalue within the q-ary Johnson bound. Consider a situation where afraction, n−n*, of positions may be pre-corrected. A tight multiplicityassignment may be used to achieve q-ary bound with respect to theeffective code length n*,

${\frac{q - 1}{q}{n^{*}\left( {1 - \sqrt{1 - {\frac{q}{q - 1}\frac{d}{n^{*}}}}} \right)}},$

The LECC bounds may be devised under the BPSK modulation and the QAMmodulation without knowledge of a noise distribution. The resultingbounds may be beyond the q-ary Johnson bound. In particular, byneglecting corruption beyond adjacent constellations in the QAMmodulation, an improved LECC bound

${n\left( {1 - {\frac{1}{3}\left( {1 + \sqrt{6\sqrt{1 - {d/n} - 2}}} \right)}} \right)}^{2}$

may be obtained.

Chase decoding (which actually refers to Chase-II decoding) may providesuitable algebraic soft-decision decoding methods for high-rateshort-to-medium-length codes. A decoding complexity associated withflipping an additional symbol may be O(n), in contrast to O(nd) bydirectly exploiting hard-decision decoding. Without applying Chaseflipping, secondary channel decisions exhibit small probabilities, thusare generally to assign small multiplicities. Chase exhaustive flippingessentially merges two most reliable channel decisions into a unifiedestimation with almost twice higher reliability, thus the multiplicitiesare literally doubled for the two most reliable channel decisions whichinvolve Chase flipping.

Let Fq be a finite field and Fq[X] be a ring of polynomials defined overFq. A given message f(x) with degree up to k−1, f(x)=f₀+f₁x+f₂x²+ . . .+f_(k-1)x^(k-1), may be encoded into a (possibly shortened) Reed-Solomoncodeword of length n by formula 1 as follows:

c=[f(1),f(α),f(α²), . . . ,f(α^(n-1))],  (1)

where α is a primitive element of Fq. Since f(x) may have up to k−1zeros, the resulting codeword may have at least n−(k−1) nonzeropositions (e.g., the minimum code weight may be d=n−k+1, a propertyknown as maximal distance separable.)

For a received channel word y=[y1, y2, . . . , yn], a (1,n−d)-weightedbivariate polynomial Q(x,y) may be defined which passes through all npoints, (x1, y1), (x2, y2), . . . , (xn, yn), each with a multiplicitym. A bivariate polynomial may be a polynomial with two variables.Passing through a point (x,y) by m times generally results in m(m+1)/2linear constraints. If the number of coefficients of Q(x,y) is greaterthan the total number of linear constraints, nm(m+1)/2, there may existnonzero solutions of Q(x,y). Let d denote a minimal (1,n−d)-weighteddegree of Q(x,y), a corresponding number of degrees of freedom isgenerally determined by formula 2 as follows:

$\begin{matrix}{N_{free} = {\left( {1 + \left\lfloor \frac{\delta}{n - d} \right\rfloor} \right)\left( {\delta + 1 - {\frac{n - d}{2}\left\lfloor \frac{\delta}{n - d} \right\rfloor}} \right)}} & (2)\end{matrix}$

A number of degrees of freedom may be satisfied by formula 3 as follows:

$\begin{matrix}\begin{matrix}{N_{free} = {\left( {\delta + 1 - {\frac{n - d}{2}\left\lfloor \frac{\delta}{n - d} \right\rfloor}} \right)\left( {\left\lfloor \frac{\delta}{n - d} \right\rfloor + 1} \right)}} \\{= {\left( {{\left( {n - t} \right)m} - {\frac{n - d}{2}\left\lfloor \frac{{\left( {n - t} \right)m} - 1}{n - d} \right\rfloor}} \right)\left( {\left\lfloor \frac{{\left( {n - t} \right)m} - 1}{n - d} \right\rfloor + 1} \right)}} \\{= {{{- \frac{n - d}{2}}\left( {\left\lfloor \frac{{\left( {n - t} \right)m} - 1}{n - d} \right\rfloor - \frac{\left( {n - t} \right)m}{n - d} + \frac{1}{2}} \right)^{2}} +}} \\{\frac{\left( {{\left( {n - t} \right)m} + {\left( {n - d} \right)/2}} \right)^{2}}{2\left( {n - d} \right)}} \\{= {\geq {\frac{n - d}{8} + \frac{\left( {{\left( {n - t} \right)m} + {\left( {n - d} \right)/2}} \right)^{2}}{2\left( {n - d} \right)}}}}\end{matrix} & (3)\end{matrix}$

where the weighted degree δ may be maximized by δ=m(n−t)−1. Anenforcement for a solvable linear system generally indicates condition 4as follows:

$\begin{matrix}{{\frac{n - d}{8} + \frac{\left( {{\left( {n - t} \right)m} + {\left( {n - d} \right)/2}} \right)^{2}}{2\left( {n - d} \right)}} > {\frac{1}{2}\mspace{14mu} {{nm}\left( {m + 1} \right)}}} & (4)\end{matrix}$

Solving with respect to t, generally results in formula 5 as follows:

t<n−√{square root over (n(n−d))},  (5)

which may achieve the general Johnson bound. The lower bound per formula3 may be tight in terms of LECC t, while affecting the value ofmultiplicity m to achieve a particular LECC t. In the sequel, formula 2may be treated as an equality. For any given t satisfying formula 5, themultiplicity m may be selected which dictates the technique complexityper formula 6 as follows:

$\begin{matrix}{m = \left\lfloor {\frac{t\left( {n - d} \right)}{\left( {n - t} \right)^{2} - {n\left( {n - d} \right)}} + 1} \right\rfloor} & (6)\end{matrix}$

Referring to FIG. 5, a graph of a multiplicity ratio 160 of theGuruswami-Sudan technique over the Wu technique is shown. The graphgenerally indicates that the Guruswami-Sudan technique may be moreefficient when a code rate is below roughly 0.25. The multiplicity ratioof the Guruswami-Sudan technique over the Wu technique for decodingReed-Solomon may code up to the Johnson bound.

A Koetter-Vardy probability transformation may be described as follows.A memoryless channel may be defined as a collection of a finite fieldalphabet Fq, an output alphabet D, and q functions per formula 7 asfollows:

f(y|x): D maps to R, for all xεFq  (7)

Upon receiving a channel word y=[y₁, y₂, . . . , y_(n)], a hard-decisionvector u=[u₁, u₂, . . . , u_(n)] may be determined per formula 8 asfollows:

$\begin{matrix}{u_{i} = {{\underset{\gamma \in {Fq}}{\arg \mspace{14mu} \max \mspace{14mu} \Pr}\left( {{X - \gamma}{Y - y_{i}}} \right)} = {\underset{\gamma \in {Fq}}{\arg \mspace{14mu} \max}\frac{f\left( {y_{i}\gamma} \right)}{\sum\limits_{x \in {Fq}}{f\left( {y_{i}x} \right)}}}}} & (8)\end{matrix}$

where the second equality generally follows from an assumption that X isuniform. A probability that a symbol X=x is transmitted given that y isobserved may be computed by formula 9 as follows:

$\begin{matrix}\begin{matrix}{{\Pr \left( {X = {\left. x \middle| Y \right. = y}} \right)} = \frac{{f\left( {yx} \right)}{\Pr \left( {X = x} \right)}}{\sum\limits_{\gamma \in F_{q}}{{f\left( {y\gamma} \right)}{\Pr \left( {X = x} \right)}}}} \\{= {\frac{f\left( {yx} \right)}{\sum\limits_{\gamma \in F_{q}}{f\left( {y\gamma} \right)}}..}}\end{matrix} & (9)\end{matrix}$

Given the received word y=[y₁, y₂, . . . , y_(n)], the a posterioriprobability matrix Π may be computed by formula 10 as follows:

π_(i,j) =Pr(X=j|y=y _(i)),  (10)

for i=1, 2, . . . , n, and j=0, 1, 2, . . . , q−1. A cost associatedwith a multiplicity matrix M_(n×q) may be given by formula 11 asfollows:

$\begin{matrix}{C = {\sum\limits_{i = 1}^{n}{\sum\limits_{j = 1}^{q}{{m_{i,j}\left( {m_{i,j} + 1} \right)}/2}}}} & (11)\end{matrix}$

A random vector M_(M)=[M₁, M₂, . . . , M_(n)] associated with amultiplicity matrix M may be defined by formula 12 as follows:

Pr(M _(i) =m _(i,j))=Pr(X=j|Y=y _(i))=π_(i,j) ,j=1,2, . . . ,q,  (12)

for i=1, 2, . . . , n. An expected score associated with the randomvector M_(M) may be defined by formula 13 as follows:

$\begin{matrix}{{E\left\{ M_{M} \right\} E\left\{ {\sum\limits_{i = 1}^{n}M_{i}} \right\}} = {\sum\limits_{i = 1}^{n}{\sum\limits_{j = 1}^{q}{m_{i,j}\pi_{i,j}}}}} & (13)\end{matrix}$

Given the reliability matrix II, the multiplicity matrix M may becomputed to maximize an expected score subject to a given maximum cost,mathematically, per formula 14 as follows:

$\begin{matrix}{{{M\left( {\prod{,C}} \right)} = {\underset{M \in {M^{\prime}{(C)}}}{\arg \mspace{14mu} \max} = {E\left\{ M_{M} \right\}}}}{where}{{M^{\prime}(C)} = {\left\{ {M \in {Z^{nxq}:{{\frac{1}{2}{\sum\limits_{i = 1}^{n}{\sum\limits_{j = 1}^{q}{m_{i,j}\left( {m_{i,j} + 1} \right)}}}} \leq C}}} \right\}.}}} & (14)\end{matrix}$

When the cost goes to infinity, the normalized multiplicity generallyconverges to the normalized reliability matrix. Furthermore, for anyscalar μ, a cost C associated with the maximum expected score M(Π,C)generally exists per formula 15 as follows:

M(Π,C)=└μΠ┘  (15)

The expected score M(Π,C) may yield a mean number of degrees of freedomper formula 16 as follows:

$\begin{matrix}{{E\left\{ N_{free} \right\}} \geq {\frac{n - d}{8} + {\frac{\left( {{E\left\{ M_{M{({\prod{,C}})}} \right\}} + {\left( {n - d} \right)/2}} \right)^{2}}{2\left( {n - d} \right)}.}}} & (16)\end{matrix}$

The Guruswami-Sudan technique generally achieves the general Johnsonbound n (1−√{square root over (1−d/n)}). The Guruswami-Sudan techniquemay also achieve the q-ary Johnson bound,

$\frac{q - 1}{q}{n\left( {1 - \sqrt{1 - {\frac{q}{q - 1}\frac{d}{n}}}} \right)}$

The bound may be achieved by assigning multiplicities pertinently acrossthe entire field Fq, whereas the original Guruswami-Sudan technique mayassign only a nontrivial multiplicity to the field element of a channelhard-decision. The Guruswami-Sudan technique may effectively achieve theq-ary Johnson bound with respect to a shortened code length byeliminating pre-corrected positions. For example,

${\frac{q - 1}{q}{n^{*}\left( {1 - \sqrt{1 - {\frac{q}{q - 1}\frac{d}{n^{*}}}}} \right)}},$

where n* generally denotes the shortened code length.

Let [c₁, c₂, . . . , c_(n)] be a transmitted Reed-Solomon codeword and[u₁, u₂, . . . , u_(n)] be a channel hard-decision vector, where u₁εFq.The Guruswami-Sudan technique may effectively achieve the q-ary Johnsonbound per formula 17 as follows:

$\begin{matrix}{t < {n\frac{q - 1}{q}{\left( {1 - \sqrt{1 - {\frac{q}{q - 1}\frac{d}{n}}}} \right).}}} & (17)\end{matrix}$

Consider a case involving t errors out of n symbols. If no softreliability information is given for each symbol, each symbol may betreated similarly with an error probability t/n. More specifically, theprobability may be defined by formula 18 as follows:

$\begin{matrix}{{\Pr \left( {c_{i} = x} \right)} = \left\{ \begin{matrix}{1 - \frac{t}{n}} & {x = u_{i}} & \; \\\frac{t}{\left( {q - 1} \right)n} & {x \neq u_{i}} & {\; {x \in F_{q}}} \\0 & {otherwise} & \;\end{matrix} \right.} & (18)\end{matrix}$

Correspondingly, multiplicities m_(i,j), i=1, 2, . . . , n, j=1, 2, . .. , q, may be assigned by formula 19 as follows:

$\begin{matrix}{m_{i,j} = \left\{ \begin{matrix}m_{1} & {if} & {j = u_{i}} \\m_{2} & {if} & {j \neq u_{i}}\end{matrix} \right.} & (19)\end{matrix}$

The multiplicities m₁ and m₂ may be treated as real numbers and considerto maximize the mean score, subject to a fixed cost. Since each positiongenerally has a similar multiplicity assignment, an adjustment of themean score with respect to a single position, for conciseness may beexpressed by formula 20 as follows:

$\begin{matrix}{{{{maxE}\left\{ M \right\}} = {{\left( {1 - \frac{t}{n}} \right)m_{1}} + {\frac{t}{n}m_{2}}}}{{{s.t.\frac{m_{1}\left( {m_{1} + 1} \right)}{2}} + {\left( {q - 1} \right)\frac{m_{2}\left( {m_{2} + 1} \right)}{2}}} = C}} & (20)\end{matrix}$

The formula 20 may be re-written as formula 21 as follows:

$\begin{matrix}{{\left( {m_{1} + \frac{1}{2}} \right)^{2} + {\left( {q - 1} \right)\left( {m_{2} + \frac{1}{2}} \right)^{2}}} = {{2C} + \frac{q}{4}}} & (21)\end{matrix}$

The notation may be simplified per formulas 22-24 as follows:

m′ ₁ =m ₁ +l/2  (22)

m′ ₂ =m ₂+½  (23)

C′=2C+q/4  (24)

Therefore, formula 20 may be re-formulate as formula 25 as follows:

$\begin{matrix}{{{maxE}\left\{ M \right\}} = {{\left( {1 - \frac{t}{n}} \right)m_{1}^{\prime}} + {\frac{t}{n}m_{2}^{\prime}} - \frac{1}{2}}} & (25)\end{matrix}$

such that m′₁ ²+(q−1)m′₂ ²=C.The formula 25 generally yields formula 26 as follows:

m′ ₁=√{square root over (C′−(q−1)m′ ₂ ²)}  (26)

Thus, formula 27 may be obtained as follows:

$\begin{matrix}{{E\left\{ M \right\}} = {{\left( {1 - \frac{t}{n}} \right)\sqrt{C^{\prime} - {\left( {q - 1} \right)m_{2}^{\prime 2}}}} + {\frac{t}{n}m_{2}^{\prime}} - \frac{1}{2}}} & (27)\end{matrix}$

Taking a derivative of formula 20 with respect to m′₂ and setting tozero, formula 28 may be obtained as follows:

$\begin{matrix}{0 = {\frac{t}{n} - {\left( {1 - \frac{t}{n}} \right) \cdot \frac{\left( {q - 1} \right)m_{2}^{\prime}}{\sqrt{C^{\prime} - {\left( {q - 1} \right)m_{2}^{\prime 2}}}}}}} & (28)\end{matrix}$

Solving formula 28 generally results in formula 29 as follows:

$\begin{matrix}{m_{2}^{\prime} = \frac{\frac{t}{n}\sqrt{C^{\prime}}}{\sqrt{{\left( {1 - \frac{t}{n}} \right)^{2}\left( {q - 1} \right)^{2}} + {\frac{t^{2}}{n^{2}}\left( {q - 1} \right)}}}} & (29)\end{matrix}$

Substituting formula 29 into formula 20, the following maximum expectedsymbol score may be given by formula 30 as follows:

$\begin{matrix}\begin{matrix}{{E\left\{ M \right\}} = {\sqrt{C^{\prime}\left( {\frac{t^{2}}{\left( {q - 1} \right)n^{2}} + \left( {1 - \frac{t}{n}} \right)^{2}} \right)} - \frac{1}{2}}} \\{= {\sqrt{\left( {{2C} + \frac{q}{4}} \right)\left( {\frac{t^{2}}{\left( {q - 1} \right)n^{2}} + \left( {1 - \frac{t}{n}} \right)^{2}} \right)} - \frac{1}{2}}}\end{matrix} & (30)\end{matrix}$

which is achieved by setting multiplicities m₁ and m₂ per formulas 31and 32 as follows:

$\begin{matrix}{m_{1} = {\frac{\left( {1 - \frac{t}{n}} \right)\left( {q - 1} \right)\sqrt{{2C} + \frac{q}{4}}}{\sqrt{{\left( {1 - \frac{t}{n}} \right)^{2}\left( {q - 1} \right)^{2}} + {\frac{t^{2}}{n^{2}}\left( {q - 1} \right)}}} - \frac{1}{2}}} & (31) \\{m_{2} = {\frac{\frac{t}{n}\sqrt{{2C} + \frac{q}{4}}}{\sqrt{{\left( {1 - \frac{t}{n}} \right)^{2}\left( {q - 1} \right)^{2}} + {\frac{t^{2}}{n^{2}}\left( {q - 1} \right)}}} - \frac{1}{2}}} & (32)\end{matrix}$

Given that t errors have occurred, the resulting score may be theexpected score, as is verified by formula 33 as follows:

$\begin{matrix}\begin{matrix}{{{nE}\left\{ M \right\}} = {\sqrt{\left( {{2C} + \frac{q}{4}} \right)\left( {\frac{t^{2}}{\left( {q - 1} \right)n^{2}} + \left( {1 - \frac{t}{n}} \right)^{2}} \right)} - \frac{n}{2}}} \\{= {\frac{{n\left( {\frac{t^{2}}{\left( {q - 1} \right)n^{2}} + \left( {1 - \frac{t}{n}} \right)^{2}} \right)}\sqrt{{2C} + \frac{q}{4}}}{\sqrt{{\left( {1 - \frac{t}{n}} \right)^{2}\left( {q - 1} \right)^{2}} + {\frac{t^{2}}{n^{2}}\left( {q - 1} \right)}}} - \frac{n}{2}}} \\{= {\frac{\left( {n - t} \right)\left( {1 - \frac{t}{n}} \right)\left( {q - 1} \right)\sqrt{{2C} + \frac{q}{4}}}{\sqrt{{\left( {1 - \frac{t}{n}} \right)^{2}\left( {q - 1} \right)^{2}} + {\frac{t^{2}}{n^{2}}\left( {q - 1} \right)}}} - \frac{n - t}{2} +}} \\{{\frac{t\frac{t}{n}\sqrt{{2C} + \frac{q}{4}}}{\sqrt{{\left( {1 - \frac{t}{n}} \right)^{2}\left( {q - 1} \right)^{2}} + {\frac{t^{2}}{n^{2}}\left( {q - 1} \right)}}} - \frac{t}{2}}} \\{= {{\left( {n - t} \right)m_{1}} + {tm}_{2}}}\end{matrix} & (33)\end{matrix}$

The expected number of degrees of freedom with respect to the givenexpected score, nE{M}, may be maximized. Therefore, a weighted(1,n−d)-degree of Q(x,y) may be maximized by formula 34 as follows:

deg _(1,n−d)(Q(x,y))=nE{M}−1  (34)

with a degree of y, denoted by Ly, may be determined by formula 35 asfollows:

$\begin{matrix}{{Ly} = {\left\lfloor \frac{\deg_{1,{n - d}}\left( {Q\left( {x,y} \right)} \right)}{n - d} \right\rfloor = \left\lfloor \left( \frac{{{nE}\left\{ M \right\}} - 1}{n - d} \right) \right\rfloor}} & (35)\end{matrix}$

Consequently, the expected number of degrees of freedom is generallymaximized per formula 36 as follows:

$\begin{matrix}\begin{matrix}{{E\left\{ N_{free} \right\}} = {{\sum\limits_{i = 0}^{L_{y}}\; 1} + {\deg_{1,{n - d}}\left( {Q\left( {x,y} \right)} \right)} - {{i\left( {n - d} \right)}L_{y}}}} \\{= {{1/2}\left( {{2{\deg_{1,{n - d}}\left( {Q\left( {x,y} \right)} \right)}} + 2 - {\left( {n - d} \right)L_{y}}} \right)\left( {L_{y} + 1} \right)}} \\{= {{1/2}\left( {{2{nE}\left\{ M \right\}} - {\left( {n - d} \right){Ly}}} \right)\left( {{Ly} + 1} \right)}} \\{= {{{- \frac{n - d}{2}}\left( {L_{y} - \frac{{nE}\left\{ M \right\}}{n - d} + \frac{1}{2}} \right)^{2}} +}} \\{\frac{\left( {{{nE}\left\{ M \right\}} + {\left( {n - d} \right)/2}} \right)^{2}}{2\left( {n - d} \right)}} \\{\geq {{- \frac{n - d}{8}} + \frac{\left( {{{nE}\left\{ M \right\}} + {\left( {n - d} \right)/2}} \right)^{2}}{2\left( {n - d} \right)}}} \\{= {\frac{\left( {{n\left( {\sqrt{\left( {{2C} + \frac{q}{4}} \right)\begin{pmatrix}{\frac{t^{2}}{\left( {q - 1} \right)n^{2}} +} \\\left( {1 - \frac{t}{n}} \right)^{2}\end{pmatrix}} - \frac{1}{2}} \right)} + {\left( {n - d} \right)/2}} \right)^{2}}{2\left( {n - d} \right)} - \frac{n - d}{8}}}\end{matrix} & (36)\end{matrix}$

The linear system may be solvable in a mean sense if the expected numberof degrees of freedom is greater than the number of linear constraints,for example, if formula 37 is true as follows:

$\begin{matrix}{{\frac{\left( {{n\left( {\sqrt{\left( {{2C} + \frac{q}{4}} \right)\left( {\frac{t^{2}}{\left( {q - 1} \right)n^{2}} + \left( {1 - \frac{t}{n}} \right)^{2}} \right)} - \frac{1}{2}} \right)} + {\left( {n - d} \right)/2}} \right)^{2}}{2\left( {n - d} \right)} - \frac{n - d}{8}} \geq {nC}} & (37)\end{matrix}$

Dividing both sides by C and letting C go to infinity, the formula 37 isgenerally reduced to formula 38 as follows:

$\begin{matrix}{{n\sqrt{2\left( {\frac{t^{2}}{\left( {q - 1} \right)n^{2}} + \left( {1 - \frac{t}{n}} \right)^{2}} \right)}} > \sqrt{2\left( {n - d} \right)n}} & (38)\end{matrix}$

Solving formula 38 generally results in formula 39 as follows:

$\begin{matrix}{t < {\frac{q - 1}{q}{n\left( {1 - \sqrt{1 - {\frac{q}{q - 1}\frac{d}{n}}}} \right)}}} & (39)\end{matrix}$

which generally achieves the q-ary Johnson bound.

Given that the LECC t satisfies formula 39, a minimum cost C whichgoverns multiplicities m₁ and m₂ as defined in formula 31 and 32,respectively may be determined. Define

$\; {{a = \sqrt{C + \frac{q}{8}}},}$

the formula in formula 37 may reduced per formula 40 as follows:

$\begin{matrix}{\left( {{a\sqrt{{2\frac{t^{2}}{q - 1}} + {2\left( {n - t} \right)^{2}}}} - \frac{d}{2}} \right)^{2} > {{2\left( {n - d} \right){n\left( {a^{2} - \frac{q}{8}} \right)}} + \frac{\left( {n - d} \right)^{2}}{4}}} & (40)\end{matrix}$

which yields formula 41 as follows:

$\begin{matrix}{{{a^{2}\left( {{2\frac{t^{2}}{q - 1}} + {2\left( {t^{2} + {nd} - {2\; n\; t}} \right)}} \right)} - {{ad}\sqrt{{2\frac{t^{2}}{q - 1}} + {2\left( {n - t} \right)^{2}}}} + \frac{n^{2}\left( {q - 1} \right)}{4} - \frac{{nd}\left( {q - 2} \right)}{4}} > 0} & (41)\end{matrix}$

Solving, a tight lower bound may be obtained per formula 42 as follows:

$\begin{matrix}{a > \frac{\begin{matrix}{{d\sqrt{\frac{t^{2}}{q - 1} + \left( {n - t} \right)^{2}}} +} \\\sqrt{\begin{matrix}{{d^{2}\left( {\frac{t^{2}}{q - 1} + \left( {n - t} \right)^{2}} \right)} - \left( {{n^{2}\left( {q - 1} \right)} - {{nd}\left( {q - 2} \right)}} \right)} \\\left( {\frac{t^{2}}{q - 1} + t^{2} + {nd} - {2{nt}}} \right)\end{matrix}}\end{matrix}}{2\sqrt{2}\left( {\frac{t^{2}}{q - 1} + t^{2} + {nd} - {2\; n\; t}} \right)}} & (42)\end{matrix}$

Therefore, set C may be the tight lower bound given in formula 43 asfollows:

$\begin{matrix}{C > {\left( \frac{\; {\begin{matrix}{{d\sqrt{\frac{t^{2}}{q - 1} + \left( {n - t} \right)^{2}}} +} \\\sqrt{\begin{matrix}{{d^{2}\left( {\frac{t^{2}}{q - 1} + \left( {n - t} \right)^{2}} \right)} -} \\{\left( {{n^{2}\left( {q - 1} \right)} - {{nd}\left( {q - 2} \right)}} \right)\left( {\frac{t^{2}}{q - 1} + t^{2} + {nd} - {2\; n\; t}} \right)}\end{matrix}}\end{matrix}\;}^{\;}}{\; {2\sqrt{2}\left( {\frac{t^{2}}{q - 1} + t^{2} + {nd} - {2n\; t}} \right)}} \right)^{2} - \frac{q}{8}}} & (43)\end{matrix}$

Consider a case where a channel hard-decision vector u has

$t < {\frac{q - 1}{q}{n\left( {1 - \sqrt{1 - {\frac{q}{q - 1}\frac{d}{n}}}} \right)}}$

errors from a transmitted codeword c. Let the multiplicity matrix M beassigned as in formula 19, where m₁, m₂ may be determined by formulas 31and 32, respectively, in conjunction with formula 42. Therefore, theerrors may be guaranteed to be list corrected by the Guruswami-Sudantechnique with the proposed multiplicity matrix M. Let the LECC be givenby formula 44 as follows:

$\begin{matrix}{t = {\left( {1 - ɛ} \right)\frac{q - 1}{q}{n\left( {1 - \sqrt{1 - {\frac{q}{q - 1}\frac{d}{n}}}} \right)}}} & (44)\end{matrix}$

Thus formula 44 generally sets two types of multiplicities per formulas45 and 46 as follows:

$\begin{matrix}{m_{1} = \frac{1 - {\left( {1 - ɛ} \right)\frac{q - 1}{q}\left( {1 - \sqrt{1 - {\frac{q}{q - 1}\frac{d}{n}}}} \right)}}{2ɛ\frac{q - 1}{q}\left( {1 - \sqrt{1 - {\frac{q}{q - 1}\frac{d}{n}}}} \right)\sqrt{1 - {\frac{q}{q - 1}\frac{d}{n}}}}} & (45) \\{m_{2} = \frac{\left( {1 - ɛ} \right)}{2\left( {q - 1} \right)ɛ\sqrt{1 - {\frac{q}{q - 1}\frac{d}{n}}}}} & (46)\end{matrix}$

Referring to FIG. 6, a graph of a cost ratio 162 of the Guruswami-Sudantechnique over the Wu technique for decoding binary BCH/Goppa codes upto the binary Johnson bound is shown. The curve 162 generally indicatesthat the Guruswami-Sudan technique is generally less efficient as thecost may be orders of magnitude than the Wu technique.

Consider a situation when some positions may be pre-corrected. Let J bethe set of pre-corrected positions. Let |J|=n−n*, where n* stands forthe shortened effective code length. Let t be the number of errorsoccurring over n* positions. Then, a hard-decision associated with oneof n* positions has an error probability of tn*. The multiplicitiesm_(i,j), i=1, 2, . . . , n, j=1, 2, . . . , q, are generally per formula47 as follows:

$\begin{matrix}{m_{i,j} = \left\{ {\begin{matrix}m_{1} & {{{{if}\mspace{14mu} j} = u_{i}},} & {i \notin J} \\m_{2} & {{{{if}\mspace{14mu} j} \neq u_{i}},} & {i \notin J} \\m_{3} & {{{{if}\mspace{14mu} j} - u_{i}},} & {i \in J} \\0 & {otherwise} & \;\end{matrix} =} \right.} & (47)\end{matrix}$

The expected score may be maximized subject to the given overall cost,per formula 48 as follows:

$\begin{matrix}{\mspace{79mu} {{{\max \; E\left\{ M \right\}} = {{\left( {n^{*} - t} \right)m_{1}} + {tm}_{2} + {\left( {n - n^{*}} \right)m_{3}}}}\mspace{11mu} \mspace{79mu} {{such}\mspace{14mu} {that}}\; \mspace{11mu} {{{n^{*}\frac{m_{1}\left( {m_{1} + 1} \right)}{2}} + {{n^{*}\left( {q - 1} \right)}\frac{m_{2}\left( {m_{2} + 1} \right)}{2}} + {\left( {n - n^{*}} \right)\frac{m_{3}\left( {m_{3} + 1} \right)}{2}}} = C}}} & (48)\end{matrix}$

A modified multiplicity m₁′ may be defined by formula 49 as follows:

$\begin{matrix}{{m_{i}^{\prime} = {m_{i} + \frac{1}{2}}},{i = 1},2,3} & (49)\end{matrix}$

A modified cost C′ may be defined by formula 50 as follows:

$\begin{matrix}{C^{\prime} = {{2C} + {\frac{{n^{*}q} + n - n^{*}}{4}.}}} & (50)\end{matrix}$

Therefore, the formula 48 may be re-formulate as formula 51 as follows:

$\begin{matrix}{{\max \; E\left\{ M \right\}} = {{\left( {n^{*} - t} \right)m_{1}^{\prime}} + {tm}_{2}^{\prime} + {\left( {n - n^{*}} \right)m_{3}^{\prime}} - \frac{n}{2}}} & (51)\end{matrix}$

such that n*m′₁ ²+n*(q−1)m′₂ ²+(n−n*)m′₃ ²=C′.By observing formula 52 as follows:

$\begin{matrix}{{{\left( {n^{*} - t} \right)m^{\prime}1} + {{tm}^{\prime}2} + {\left( {n - n^{*}} \right)m^{\prime}3}} = {{{{\sum\limits_{i = 1}^{n^{*}}\; {\frac{n^{*} - t}{n^{*}}m_{1}^{\prime \;}}} + {\sum\limits_{i = 1}^{n^{*}{({q - 1})}}{\frac{t}{\left( {q - 1} \right)n^{*}}m_{2}^{\prime}}} + {\sum\limits_{i = 1}^{n^{*}n}m_{3}^{\prime}}} \leq {\left( {{\sum\limits_{i = 1}^{n^{*}}\frac{\left( {n^{*} - t} \right)^{2}}{n^{*2}}} + {\sum\limits_{i = 1}^{n^{*}{({q - 1})}}\frac{t^{2}}{\left( {q - 1} \right)^{2}n^{*2}}} + {\sum\limits_{i = 1}^{n - n^{*}}1}} \right)^{\frac{1}{2}} \cdot \left( {{\sum\limits_{i = 1}^{n^{*}}m_{1}^{\prime 2}} + {\sum\limits_{i = 1}^{n^{*}{({q - 1})}}m_{2}^{\prime 2}} + {\sum\limits_{i = 1}^{n - n^{*}}m_{3}^{\prime 2}}} \right)^{\frac{1}{2}}}} = {\left( {\frac{\left( {n^{*} - t} \right)^{2}}{n^{*}} + \frac{t^{2}}{\left( {q - 1} \right)n^{*}} + \left( {n - n^{*}} \right)} \right)^{\frac{1}{2}} \cdot C^{\prime \frac{1}{2}}}}} & (52)\end{matrix}$

where “=” may be achieved if and only if

$\frac{m_{1}^{\prime}}{\frac{n^{*} - t}{n^{*}}} = {\frac{m_{2}^{\prime}}{\frac{t}{\left( {q - 1} \right)n^{*}}} = {m_{3}^{\prime}.}}$

To simplify notation, a parameter Δt may be defined by formula 53 asfollows:

$\begin{matrix}{\Delta_{t} = {\frac{\left( {n^{*} - t} \right)^{2}}{n^{*}} + \frac{t^{2}}{\left( {q - 1} \right)n^{*}} + \left( {n - n^{*}} \right)}} & (53)\end{matrix}$

Therefore, E{M} may be maximized per formula 54 as follows:

$\begin{matrix}{{E\left\{ M \right\}} = {{\Delta_{t}^{\frac{1}{2}}C^{\prime^{\frac{1}{2}}}} - \frac{n}{2}}} & (54)\end{matrix}$

by setting the multiplicities per formulas 55-57 as follows:

$\begin{matrix}{m_{3} = {{m_{3}^{\prime} - \frac{1}{2}} = {{\Delta_{t}^{- \frac{1}{2}}C^{\,_{\prime}\frac{1}{2}}} - \frac{1}{2}}}} & (55) \\{m_{1} = {{\frac{n^{*} - t}{n^{*}}m_{3}^{\prime}} - \frac{1}{2}}} & (56) \\{m_{2} = {{\frac{t}{\left( {q - 1} \right)n^{*}}m_{3}^{\prime}} - \frac{1}{2}}} & (57)\end{matrix}$

Following formula 36, the expected number of degrees of freedom may bemaximized to a lower bound per formula 58 as follows:

$\begin{matrix}\begin{matrix}{{E\left\{ N_{free} \right\}} = {\frac{n - d}{8} + \frac{\left( {{E\left\{ M \right\}} + {\left( {n - d} \right)/2}} \right)^{2}}{2\left( {n - d} \right)}}} \\{= {\frac{n - d}{8} + \frac{\left( {{\Delta_{t}^{\frac{1}{2}}C^{\,_{\prime}\frac{1}{2}}} - \frac{d}{2}} \right)^{2}}{2\left( {n - d} \right)}}}\end{matrix} & (58)\end{matrix}$

The enforcement that the number of degrees of freedom is greater thanthe cost may become formula 59 as follows:

$\begin{matrix}{{\frac{\left( {{\Delta_{t}^{\frac{1}{2}}C^{\,_{\prime}\frac{1}{2}}} - \frac{d}{2}} \right)^{2}}{2\left( {n - d} \right)} - \frac{n - d}{8}} > C} & (59)\end{matrix}$

Dividing both sides of formula 59 by C and letting C go to infinityyields formula 60 as follows:

$\begin{matrix}{\Delta_{t} = {{\frac{\left( {n^{*} - t} \right)^{2}}{n^{*}} + \frac{t^{2}}{\left( {q - 1} \right)n^{*}} + \left( {n - n^{*}} \right)} > \left( {n - d} \right)}} & (60)\end{matrix}$

Solving formula 60 may result in formula 61 as follows:

$\begin{matrix}{t < {\frac{q - 1}{q}{n^{*}\left( {1 - \sqrt{1 - {\frac{q}{q - 1}\frac{d}{n^{*}}}}} \right)}}} & (61)\end{matrix}$

which generally matches the q-ary Johnson bound with respect toeffective code length n*.

A minimum cost C to enforce an LECC t satisfying formula 61 may bedetermined as follows. By defining

${a = \sqrt{C + \frac{{n^{*}q} + n - n^{*}}{8}}},$

the formula 59 may reduced to formula 62 as follows:

$\begin{matrix}{{{a^{2}2\left( {\frac{\left( {n^{*} - t} \right)^{2}}{n^{*}} + \frac{t^{2}}{\left( {q - 1} \right)n^{*}} + n^{*} - d} \right)} - {a\sqrt{2}d\; \Delta_{t}^{\frac{1}{2}}} + {\frac{n - d}{4}{n^{*}\left( {q - 1} \right)}} + \frac{n\; d}{4}} > 0} & (62)\end{matrix}$

A tight lower bound may thus exist per formula 63 as follows:

$\begin{matrix}{a > \frac{{d\sqrt{\Delta_{t}}} + \sqrt{\begin{matrix}{{d^{2}\Delta_{t}} - \left( {{n\; d} + {\left( {n - d} \right){n^{*}\left( {q - 1} \right)}}} \right)} \\\left( {\frac{\left( {n^{*} - t} \right)^{2}}{n^{*}} + \frac{t^{2}}{\left( {q - 1} \right)n^{*}} + n^{*} - d} \right)\end{matrix}}}{{2\sqrt{2}\left( {\frac{\left( {n^{*} -} \right)t^{2}}{n^{*}},n^{*}} \right)} + \frac{t^{2}}{\left( {q - 1} \right)n^{*}} + n^{*} - d}} & (63)\end{matrix}$

Therefore, in order to achieve an LECC t satisfying formula 63, the costC may be set per formula 64 as follows:

$\begin{matrix}{C > {{- \frac{{n^{*}q} + n - n^{*}}{8}} + \left( \frac{{d\sqrt{\Delta_{t}}} + \sqrt{\frac{{d^{2}\Delta_{t}} - \left( {{n\; d} + {\left( {n - d} \right){n^{*}\left( {q -} \right)}}} \right)}{\left( {\frac{\left( {n^{*} - t} \right)^{2}}{n^{*}} + \frac{t^{2}}{\left( {q - 1} \right)n^{*}} + n^{*} - d} \right)}}}{{2\sqrt{2}\left( {\frac{\left( {n^{*} -} \right)t^{2}}{n^{*}},n^{*}} \right)} + \frac{t^{2}}{\left( {q - 1} \right)n^{*}} + n^{*} - d} \right)^{2}}} & (64)\end{matrix}$

After C is determined, the multiplicities m₁, m₂, and m₃ may be computedfollowing formulas 55, 56 and 57, respectively.

Referring to FIG. 7 a graph of ratios of q-ary list decodingcapabilities to minimum distances as a function of ratio of minimumdistances to the effective code length is shown. Consider a case where achannel hard-decision vector u may be partially pre-corrected such thatthe positions in a set J(|J|=n−n*) may be pre-corrected. Furthermore,consider that u may have

$t < {\frac{q - 1}{q}{n^{*}\left( {1 - \sqrt{1 - {\frac{q}{q - 1}\frac{d}{n^{*}}}}} \right)}}$

errors from the n* uncertain positions. The multiplicity matrix M may beassigned as in formula 47, where the multiplicities m₁, m₂ and m₃, aregenerally determined by formula 55, 56 and 57, respectively, inconjunction with formula 64. Thus, the errors may be list corrected bythe Guruswami-Sudan technique with the proposed multiplicity matrix. Assuch, the effective code length n* may be reduced toward a nontrivialq-ary minimum code length

$\frac{q}{q - 1}d$

and the LECC t generally converges to d, as illustrated in FIG. 7. Thecurve 164 may illustrate a binary Johnson bound. The curve 166 generallyillustrates a ternary Johnson bound. The curve 168 may represent aquaternary Johnson bound. Furthermore, the curve 170 may illustrate ageneral Johnson bound.

The LECC may be enhanced by incorporating various modulation methods.Consider a binary phase-shift keying (e.g., BPSK) modulation of aReed-Solomon code over F₂w. Let [c₁, c₂, . . . , c_(n)] be a transmittedReed-Solomon codeword and [u₁, u₂, . . . ,u_(n)] be a channelhard-decision vector, where u_(i) εF₂w. Consider a situation with terrors out of n symbols. If no soft reliability data is given for eachsymbol, each symbol may be treated equally with an error probabilityt/n. Consequently, the bit error probability, p_(b), generally satisfiesformula 65 as follows:

$\begin{matrix}{{1 - \frac{t}{n}} = {\left. \left( {1 - p_{b}} \right)\Leftrightarrow p_{b} \right. = {1 - \left( {1 - \frac{t}{n}} \right)^{\frac{1}{w}}}}} & (65)\end{matrix}$

and symbol transition probabilities may be given by formula 66 asfollows:

Pr(c _(i) =x)=p _(b) ^(l)(1−p _(b))^(l/w)  (66)

where l=w(c_(i)⊕x) and w(γ) generally denotes the Hamming weight of thebinary representation of γεF₂w. Correspondingly, the multiplicitiesm_(i,j), i=1, 2, . . . , n, j=1, 2, . . . , q, may be assigned byformula 67 as follows:

m _(i,j) =m ₁, if l=w(j⊕u _(i))  (67)

The multiplicities m_(l), l=0, 1, 2, . . . , w, may be treated as realnumbers and considered to maximize the mean score, subject to a fixedcost. Since each position has a similar multiplicity assignment, animprovement of the mean score with respect to a single position, forconciseness, may be given by formula 68 as follows:

$\begin{matrix}{{{\max \; E\left\{ M \right\}} = {\sum\limits_{l = 0}^{w}{\left( \frac{w}{l} \right){p_{b}^{l}\left( {1 - p_{b}} \right)}^{w - l}m_{l}}}}{{{such}\mspace{14mu} {that}\mspace{14mu} {\sum\limits_{l = 0}^{w}{\left( \frac{w}{l} \right)\frac{m_{l}\left( {m_{l} + 1} \right)}{2}}}} = C}} & (68)\end{matrix}$

The modified multiplicity m′_(l) may be defined by formula 69 asfollows:

m′ _(l) =ml+½, l=0, 1, 2, . . . ,w  (69)

Formula 69 may be re-written into formula 70 as follows;

$\begin{matrix}{{\max \; E\left\{ M \right\}} = {{\sum\limits_{1 = 0}^{w}{\left( \frac{w}{l} \right){p_{b}^{l}\left( {1 - p_{b}} \right)}^{w - l}m_{l}^{\prime}}} - {\frac{1}{2}.}}} & (70) \\{{{such}\mspace{14mu} {that}\mspace{14mu} {\sum\limits_{l = 0}^{w}{\left( \frac{w}{l} \right)m_{l}^{\prime^{2}}}}} = {{2C} + {2^{w - 2}.}}} & \;\end{matrix}$

Solving generally produces formula 71 as follows:

$\begin{matrix}{{E\left\{ M \right\}} \leq {{- \frac{1}{2}} + {\left( {\sum\limits_{l = 0}^{w}{\left( \frac{w}{l} \right){p_{b}^{2l}\left( {1 - p_{b}} \right)}^{2{({w - l})}}}} \right)^{\frac{1}{2}}\left( {{2C} + 2^{w - 2}} \right)^{\frac{1}{2}}}}} & (71)\end{matrix}$

where “=” may be achieved if and only if formula 72 is true as follows:

$\begin{matrix}{\frac{m_{l}^{\prime}}{{p_{b}^{l}\left( {1 - p_{b}} \right)}^{w - l}} = \sqrt{\frac{{2C} + 2^{w - 2}}{\sum\limits_{j = 0}^{w}{\left( \frac{w}{j} \right){p_{b}^{2j}\left( {1 - p_{b}} \right)}^{2{({w - j})}}}}}} & (72)\end{matrix}$

for l=0, 1, 2, . . . , w.

The enforcement that the number of degrees of freedom to be more thanthe number of linear constraints yields formula 73 as follows:

$\begin{matrix}{{\frac{\left( {{n\left( {\sqrt{\left( {{2C} + 2^{w - 2}} \right)\left( {\sum\limits_{l = 0}^{ù}{\left( \frac{w}{l} \right){p_{b}^{2l}\left( {1 - p_{b}} \right)}^{2{({w - l})}}}} \right)} - \frac{1}{2}} \right)} + {\left( {n - d} \right)/2}} \right)^{2}}{2\left( {n - d} \right)} - \frac{n - d}{8}} > {nC}} & (73)\end{matrix}$

Dividing both sides of the formula 73 by C and letting C go to infinitygenerally yields formula 74 as follows:

$\begin{matrix}{{\sum\limits_{l = 0}^{w}\; {\left( \frac{w}{l} \right){p_{b}^{2l}\left( {1 - p_{b}} \right)}^{2{({w - l})}}}} > {1 - {\frac{d}{n}.}}} & (74)\end{matrix}$

Furthermore, the multiplicities may be per formula 75 as follows:

$\begin{matrix}{\frac{m_{0}^{\prime}}{1 - \frac{t}{n}} = {\frac{m_{i}^{\prime}}{\frac{t}{\left( {2 - 1} \right)n}} = \sqrt{\frac{{2C} + 2^{w - 2}}{{\left( {1 - \frac{t}{n}} \right)^{2}\left( {q - 1} \right)^{2}} + {\frac{t^{2}}{n^{2}}\left( {q - 1} \right)}}}}} & (75)\end{matrix}$

for i=1, 2, . . . , w. Thus, the resulting LECC may achieve a similarq-ary Johnson bound. Note that formula 75 is generally a pessimisticmultiplicity assignment, therefore, the resulting LECC bound governed byformula 74 may be beyond q-ary Johnson bound.

The QAM modulation may be considered. To ease analysis, each codewordsymbol may be considered to be centered in the alphabet, which aregenerally the most prone to corruption. For t symbols that may beerroneous, an error probability on each dimension, denoted by p_(q),generally satisfies formula 76 as follows:

$\begin{matrix}{{1 - \frac{t}{n}} = {\left. \left( {1 - {2p_{q}}} \right)^{2}\Leftrightarrow p_{q} \right. = {\frac{1}{2} - {\frac{1}{2}{\sqrt{1 - \frac{t}{n}}.}}}}} & (76)\end{matrix}$

Given a noise distribution, an a posteriori probability that y isreceived conditioned on the transmission of x, Pr(x|y) may becalculated. A simplification may be made that the corruption only incurto the 8 nearest neighbors, which is a dominant pattern for the typicalGaussian noise. The simplification generally renders the analysisindependent of the noise model and loosens an assumption on atransmitted symbol such that the symbol is not on the edge of signalconstellation.

Utilizing the methodology, formula 77 may be obtained as follows:

$\begin{matrix}{{\frac{\left( {{n\left( {{\left( {\left( {1 - {2p_{q}}} \right)^{2} + {2p_{q}^{2}}} \right)\sqrt{\left( {{2C} + \frac{q}{4}} \right)}} - \frac{1}{2}} \right)} + {\left( {n - d} \right)/2}} \right)^{2}}{2\left( {n - d} \right)} - \frac{n - d}{8}} > {nC}} & (77)\end{matrix}$

The associated multiplicities, m₀, m₁ and m₂ may be set per formula 78as follows:

$\begin{matrix}{\frac{m_{0} + \frac{1}{2}}{\left( {1 - {2p_{q}}} \right)^{2}} = {\frac{m_{1} + \frac{1}{2}}{p_{q} - {2p_{q}^{2}}} = {\frac{m_{2} + \frac{1}{2}}{p_{q}^{2}} = {\frac{\sqrt{{2C} + \frac{q}{4}}}{\left( {1 - {2p_{q}}} \right)^{2} + {2p_{q}^{2}}}.}}}} & (78)\end{matrix}$

Accordingly, p_(q) generally satisfies, with a large enough cost C,formula 79 as follows:

$\begin{matrix}{\left( {\left( {1 - {2p_{q}}} \right)^{2} + p_{q}^{2}} \right)^{2} > {1 - \frac{d}{n}}} & (79)\end{matrix}$

Solving formula 79 generally results in formula 80 as follows:

$\begin{matrix}{\left( {\left( {1 - {2p_{q}}} \right)^{2} + p_{q}^{2}} \right)^{2} > {1 - \frac{d}{n}}} & (80)\end{matrix}$

which yields formula 81 as follows:

$\begin{matrix}{t < {{n\left( {1 - {\frac{1}{3}\left( {1 + \sqrt{{6\sqrt{1 - \frac{d}{n}}} - 2}} \right)^{2}}} \right)}.}} & (81)\end{matrix}$

Generally, p_(q) may be unconstrained and consequently the bound on tmay be maximized to t<d when

${{{6\sqrt{1 - \frac{d}{n}}} - 2} < 0},$

for example d/n>8/9. By limiting error corruption to 8 nearestneighbors, a q-ary Reed-Solomon code may be treated as a 9-ary code,which has the natural bound on the minimum distance, d/n≦8/9.

The Chase decoding technique, referred to as a variant of Chase IItechnique, may flip all combinations of a set of t symbol errorpatterns. Each time the Chase technique may apply bounded-distancedecoding and choose the most likely codeword, if any, among the decodedcandidate codewords.

Let c=[c₁, c₂, . . . , c_(n)] be a transmitted codeword and y=[y₁, y₂, .. . , y_(n)] be the received channel word. Let u=[u₁, u₂, . . . , u_(n)]be the hard-decision vector as defined in formula 8. Let u,_(i),iεI={i₁, i₂, . . . , i_(τ)} be τ (|I|=τ) secondary (or second) mostreliable channel decisions, such that formula 82 is satisfied asfollows:

π_(i,u′i)≧π_(j,i) , iεI, j≠u _(l)  (82)

A base multiplicity matrix M may be defined by constraint 83 as follows:

m _(i,ui) =m _(i,u′i) , iεI  (83)

The term “a” preferred combination may be used where, as the preferenceis generally subject to the constraint 83. Selection criteria 82 justifythat constraint 83 is nearly true.

Multiple (e.g., 2 ^(τ)) multiplicity matrices for Chase exhaustiveflipping may be defined by formula 84 as follows:

$\begin{matrix}\left\{ \begin{matrix}{{m_{i,j}^{({{b\; 1},{b\; 2},\ldots \mspace{14mu},{b\; \tau}})} = 0},} & {{{{if}\mspace{14mu} i} = {i_{1} \in I}},{j = u_{i}},{b_{1} = 0}} \\{{m_{i,j}^{({{b\; 1},{b\; 2},\ldots \mspace{14mu},{b\; \tau}})} = 0},} & {{{{if}\mspace{14mu} i} = {i_{1} \in I}},{j = u_{1}^{\prime}},{b_{1} = 1}} \\{{m_{i,j}^{({{b\; 1},{b\; 2},\ldots \mspace{14mu},{b\; \tau}})} = m_{i,j}},} & {otherwise}\end{matrix} \right. & (84)\end{matrix}$

where b_(l)ε{0,1} generally denotes the indicator of the l-th flippingsymbol, with 0 meaning to bet on u_(il), whereas 1 to bet on u′_(il).

A new reliability matrix Π′ may be defined by formula 85 as follows

$\begin{matrix}{\prod^{\prime}{= \left\{ \begin{matrix}{\pi_{i,j}^{\prime} = \pi_{i,j}} & {{{{if}\mspace{14mu} i} \in I},} & {{{or}\mspace{14mu} j} \neq \left\{ {u_{i},u_{i}^{\prime}} \right\}} \\{\pi_{i,j}^{\prime} = {\pi_{i,j} + \pi_{i,{u^{\prime}i}}}} & {{{{if}\mspace{14mu} i} \in I},} & {c_{i} \in \left\{ {u_{i},u_{i}^{\prime}} \right\}} \\{\pi_{i,j}^{\prime} = {\pi_{i,j} + \pi_{i,{u^{\prime}i}}}} & {{{{if}\mspace{14mu} i} \in I},} & {c_{i} \neq \left\{ {u_{i},u_{i}^{\prime}} \right\}} \\{\pi_{i,j}^{\prime} = 0} & {otherwise} & \;\end{matrix} \right.}} & (85)\end{matrix}$

and the corresponding multiplicity matrix M′ may be defined by formula86 as follows:

$\begin{matrix}{M^{\prime} = \left\{ \begin{matrix}{m_{i,j}^{\prime} = 0} & {{{{if}\mspace{14mu} i} \in I},{j \in \left\{ {u_{i},u_{i}^{\prime}} \right\}},{\pi_{i,j}^{\prime} = 0}} \\{m_{i,j}^{\prime} = m_{i,j}} & {otherwise}\end{matrix} \right.} & (86)\end{matrix}$

The reliability matrix Π′ is generally unavailable, as the genieinformation of the transmitted codeword may not be available.

Consider a combination of Chase exhaustive flipping and Koetter-Vardymutliplicities. Let M^((b1, b2, . . . , bt)), b_(i)ε{0,1}, be 2^(τ)multiplicity matrices for Chase combinatorial list decoding attempts.Hence, a probability of failure may be similar to the single listdecoding with the multiplicity matrix M′ over the reliability matrix Π′.

Consider formula 87 as follows:

C(M ^((b1, b2, . . . ,bt)))=C(M′)  (87)

A matric ΔM′ may be defined per formula 88 as follows:

$\begin{matrix}{{\Delta \; M^{\prime}} = \left\{ {\delta \in {Z:{{\left( {\delta + 1 - {\frac{n - d}{2}\left( \frac{\overset{¨}{a}}{n - d} \right)}} \right)\left( {\left( \frac{\delta}{n - d} \right) + 1} \right)} > {C\left( M^{\prime} \right)}}}} \right\}} & (88)\end{matrix}$

and the score of a vector v=[v₁, v₂, . . . , v_(n)] associated with amultiplicity matrix M may be given by formula 89 as follows:

$\begin{matrix}{{S_{M}(v)} = {\sum\limits_{i = 1}^{n}\; {m_{i,v_{i}}.}}} & (89)\end{matrix}$

Therefore, the transmitted codeword c may be list decodable by themerged multiplicity matrix M′ if S_(M′) (c)>Δ(M′).

Due to an exhaustiveness of Chase flipping, a flipping pattern (b₁, b₂,. . . , b_(t)) generally exists per formula 90 as follows:

M′=M ^((b1, b2, . . . , bt))  (90)

consequently, formula 91 is applicable as follows:

S _(M′)(c)=S _(M) ^((b1, b2, . . . , bt))(c)  (91)

A Chase exhaustive flipping may be treated as a single attempt withrespect to a merged reliability matrix. Consequently, the basemultiplicity matrix M may be obtained by means of a virtual reliabilitymatrix Π″, utilizing formula 92 as follows:

M=└μΠ′┘  (92)

where μ>1 may be a scalar and Π″ is generally defined by formula 93 asfollows:

$\begin{matrix}{\prod^{''}{= \left\{ \begin{matrix}{\pi_{i,j}^{''} = \pi_{i,j}} & {{{if}\mspace{14mu} i} \notin I} & {{{or}\mspace{14mu} j} \neq \left\{ {u_{i},u_{i}^{\prime}} \right\}} \\{p_{i,j}^{''} = {\pi_{i,{ui}} + \pi_{i,{u^{\prime}i}}}} & {{{{if}\mspace{14mu} i} \in I},} & {j \in \left\{ {u_{i},u_{i}^{\prime}} \right\}}\end{matrix} \right.}} & (93)\end{matrix}$

Note that Π″ may not be a valid reliability matrix, due to formula 94 asfollows:

$\begin{matrix}{{{\sum\limits_{j = 0}^{q}\; \pi_{i,j}^{''}} = {{1 + \pi_{i,u_{i}} + \pi_{i,u_{i}^{\prime}}} > 1}},{i \in I}} & (94)\end{matrix}$

The probability distributions π_(i,ui) and π_(i,u′i) may be close to 0.5and thus may be assigned roughly half a maximum multiplicity. On theother hand, Chase flipping essentially merges the two most reliablechannel estimations into a unified estimation with almost twice higherreliability, thus the near maximum multiplicities, may be assigned tothe chosen estimation and zero to the other estimation.

By enforcing a message polynomial p(x) with zero constant term, p₀=0,the decoder complexity may be dramatically reduced at negligibleperformance degradation. More specifically, a candidate bivariatepolynomial Q(x,y) is generally invalid if a constant term is nonzero(e.g., Q(0,0)≠0) and a spurious term has roughly a q⁻¹ probability tocontain a zero constant term. As a result, the constant term Q(0,0)associated with a Chase flipping pattern may be tracked and thecorresponding bivariate polynomial Q(x,y) may be constructed andfactorized if Q(0,0)=0. Such enforcement may be similar to reducing acode redundancy by unity (1). The same strategy may be deployed for theproposed general framework of Chase decoding which incorporatesKoetter-Vardy varying multiplicity.

For high-rate codes of dominant practical interest, the encoding processutilizing may be less efficient than the an encoding process utilizinglinear-feedback-shift-registers (e.g., LFSR), based on a generatorpolynomial. Thus, a generator polynomial may enable the LFSR encoding.

A subcode of an (n,k) Reed-Solomon code over Fq may be defined such thatcorresponding message polynomials p(x) satisfy p₀=0 and deg(p(x))<k maybe an (n,k−1) Reed-Solomon code associated with generator polynomial performula 95 as follows:

G(x)=(x−1)(x−α)(x−α ²) . . . (x−α ^(n-k))  (95)

where α may be a primitive element of Fq.

A message polynomial p(x) may have a degree of up to k−1. The primitive(e.g., n=q−1) codeword polynomial c(x) may be given by formula 96 asfollows:

$\begin{matrix}\begin{matrix}{{c(x)} = {{p(1)} + {{p(\alpha)}x} + {{p\left( \alpha^{2} \right)}x^{2}} + \ldots + {{p\left( \alpha^{q - 2} \right)}x^{q - 2}}}} \\{= {{\sum\limits_{i = 1}^{k - 1}\; p_{i}} + {x{\sum\limits_{i = 1}^{k - 1}\; {p_{i}\alpha^{i}}}} + \ldots + {x^{q - 2}{\sum\limits_{i = 1}^{k - 1}\; {p_{i}\alpha^{i{({q - 2})}}}}}}} \\{= {\sum\limits_{i = 1}^{k - 1}\; {p_{i}\left( {1 + {x\; \alpha^{i}} + {x^{2}\alpha^{2i}} + \ldots + {x^{q - 2}\alpha^{i{({q - 2})}}}} \right)}}} \\{= {\sum\limits_{i = 1}^{k - 1}\; {p_{i}\frac{1 - x^{q - 1}}{1 - {\alpha^{i}x}}}}} \\{= {\sum\limits_{i = 1}^{k - 1}\; {\alpha^{- i}p_{i}\frac{\left( {x - 1} \right)\left( {x - \alpha} \right)\left( {x - \alpha^{2}} \right)\mspace{14mu} \ldots \mspace{14mu} \left( {x - \alpha^{q - 2}} \right)}{x - \alpha^{- i}}}}}\end{matrix} & (96)\end{matrix}$

which indicates that c(x) contains (consecutive) roots 1, α, α², . . . ,α^(q-1-k). The two codes may have the same dimension, k−1.

Referring to FIG. 8 a graph of simulation performance comparisons fordecoding a (458, 410) Reed-Solomon code over F₂ ₁₀ under a BPSKmodulation is shown. A curve 172 may illustrate a hard-decision decodeperformance. A curve 174 may illustrate a KV(μ=4.99) decode performance.Curve 176 generally illustrates a Chase-10 decode performance. A curve178 may illustrate a KV(μ=2.99)+Chase-10 decode performance. Curve 180generally illustrates a KV(μ=3.00)+Chase-10 decode performance. A curve182 may show a Wu(C=100)+Chase-10 decode performance. Curve 184generally illustrates a KV(μ=infinity) performance. A curve 186 mayrepresent a KV(μ=infinity)+Chase-10 performance.

The hard-decision decoding may correct up to 24 errors. The conventionalChase-10 decoding, which systematically flips the 10 most reliablesecond symbol decisions, may provide a 0.3 dB improvement overhard-decision decoding. The Koetter-Vary technique with a maximummultiplicity 4 generally exhibits similar performance as the Chase-10decoding. The Koetter-Vary technique with a maximum multiplicity 2 incombination with the Chase-10 flipping may be less complex than theKoetter-Vardy technique with the maximum multiplicity 4, while achievingbetter performance. The Wu technique with maximum cost of 100 andChase-10 flipping, generally performs performance and may be lesscomplex than Koetter-Vardy (μ=3.99)+Chase-10 decoding. The performanceof the Koetter-Vardy (μ=infinity)+Chase-10 decoding is generally 0.15 dBfarther from that of Koetter-Vardy (μ=infinity) alone, which has anapproximately 0.5 gain over the hard-decision decoding.

Referring to FIG. 9, a graph os simulation performance comparisons fordecoding a (255, 191) Reed-Solomon code over F₂ ₈ under a QAM modulationis shown. A curve 190 generally illustrates a hard-decision decodeperformance. A curve 192 may illustrate a Chase-11 decode performance.Curve 194 generally shows a Chase-16 decode performance. A curve 196 mayillustrate a KV(μ=6.99) decode performance. A curve 198 may illustrate aKV(μ=2.99)+Chase-11 decode performance. Curve 200 generally illustratesa KV(μ=3.99)+Chase-11 decode performance. A curve 202 may show aKV(μ=3.00)+Chase-16 decode performance. Curve 204 generally illustratesa Wu(C=150)+Chase-11 performance. A curve 206 may represent aWu(C=300)+Chase 16 decode performance. Curve 208 generally illustrates aKV(μ=infinity) performance. A curve 210 may show aKV(μ=infinity)+Chase-16 performance.

A Berlekamp-Massey hard-decision technique generally corrects up to 32errors. The Chase-11 decoding, with a complexity increase by a factor of2¹¹=2048, yields about a 0.5 dB improvement over the hard-decisiondecoding. The Chase-16 decoding, which further scales complexity by afactor of 2⁵=32, generally yields an additional 0.15 dB. The additionalgain of Chase decoding may diminish by flipping more and more of themost reliable second symbol decisions. The Koetter-Vardy(μ=3.99)+Chase-11 decoding may exhibit a good performance and is lesscomplex than the Koetter-Vardy technique with μ=6.99. The Koetter-Vardy(μ=3.99)+Chase-16 decoding gains 0.9 dB over the hard-decision decodingand is just 0.2 dB away from the Koetter-Vardy technique with μ=8. Theperformance of the Koetter-Vardy (μ=infinity)+Chase-16 decodinggenerally achieves a 0.35 dB improvement over the original Koetter-Vardy(μ=infinity) and a 1.4 dB improvement over the hard-decision decodingperformance.

The functions performed by the diagrams of FIGS. 1-4 may be implementedusing one or more of a conventional general purpose processor, digitalcomputer, microprocessor, microcontroller, RISC (reduced instruction setcomputer) processor, CISC (complex instruction set computer) processor,SIMD (single instruction multiple data) processor, signal processor,central processing unit (CPU), arithmetic logic unit (ALU), videodigital signal processor (VDSP) and/or similar computational machines,programmed according to the teachings of the present specification, aswill be apparent to those skilled in the relevant art(s). Appropriatesoftware, firmware, coding, routines, instructions, opcodes, microcode,and/or program modules may readily be prepared by skilled programmersbased on the teachings of the present disclosure, as will also beapparent to those skilled in the relevant art(s). The software isgenerally executed from a medium or several media by one or more of theprocessors of the machine implementation.

The present invention may also be implemented by the preparation ofASICs (application specific integrated circuits), Platform ASICs, FPGAs(field programmable gate arrays), PLDs (programmable logic devices),CPLDs (complex programmable logic devices), sea-of-gates, RFICs (radiofrequency integrated circuits), ASSPs (application specific standardproducts), one or more monolithic integrated circuits, one or more chipsor die arranged as flip-chip modules and/or multi-chip modules or byinterconnecting an appropriate network of conventional componentcircuits, as is described herein, modifications of which will be readilyapparent to those skilled in the art(s).

The present invention thus may also include a computer product which maybe a storage medium or media and/or a transmission medium or mediaincluding instructions which may be used to program a machine to performone or more processes or methods in accordance with the presentinvention. Execution of instructions contained in the computer productby the machine, along with operations of surrounding circuitry, maytransform input data into one or more files on the storage medium and/orone or more output signals representative of a physical object orsubstance, such as an audio and/or visual depiction. The storage mediummay include, but is not limited to, any type of disk including floppydisk, hard drive, magnetic disk, optical disk, CD-ROM, DVD andmagneto-optical disks and circuits such as ROMs (read-only memories),RAMS (random access memories), EPROMs (erasable programmable ROMs),EEPROMs (electrically erasable programmable ROMs), UVPROM (ultra-violeterasable programmable ROMs), Flash memory, magnetic cards, opticalcards, and/or any type of media suitable for storing electronicinstructions.

The elements of the invention may form part or all of one or moredevices, units, components, systems, machines and/or apparatuses. Thedevices may include, but are not limited to, servers, workstations,storage array controllers, storage systems, personal computers, laptopcomputers, notebook computers, palm computers, personal digitalassistants, portable electronic devices, battery powered devices,set-top boxes, encoders, decoders, transcoders, compressors,decompressors, pre-processors, post-processors, transmitters, receivers,transceivers, cipher circuits, cellular telephones, digital cameras,positioning and/or navigation systems, medical equipment, heads-updisplays, wireless devices, audio recording, audio storage and/or audioplayback devices, video recording, video storage and/or video playbackdevices, game platforms, peripherals and/or multi-chip modules. Thoseskilled in the relevant art(s) would understand that the elements of theinvention may be implemented in other types of devices to meet thecriteria of a particular application.

The terms “may” and “generally” when used herein in conjunction with“is(are)” and verbs are meant to communicate the intention that thedescription is exemplary and believed to be broad enough to encompassboth the specific examples presented in the disclosure as well asalternative examples that could be derived based on the disclosure. Theterms “may” and “generally” as used herein should not be construed tonecessarily imply the desirability or possibility of omitting acorresponding element.

While the invention has been particularly shown and described withreference to the preferred embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade without departing from the scope of the invention.

1. An apparatus comprising: a first circuit configured to generate (i) aplurality of symbols and (ii) a plurality of decision values both inresponse to detecting an encoded codeword; and a second circuitconfigured to (i) generate a plurality of probabilities to flip one ormore of said symbols based on said decision values, (ii) generate amodified probability by merging two or more of said probabilities of anunreliable position in said symbols and (iii) generate a decodedcodeword by decoding said symbols using an algebraic soft-decisiontechnique in response to said modified probability.
 2. The apparatusaccording to claim 1, wherein (i) said probabilities are generated usinga Chase technique and (ii) said algebraic soft-decision techniquecomprises a Koetter-Vardy technique.
 3. The apparatus according to claim1, wherein said encoded codeword comprises one of (i) a Reed-Solomonencoded codeword and (ii) a BCH encoded codeword.
 4. The apparatusaccording to claim 1, wherein said detecting comprises a soft detecting.5. The apparatus according to claim 1, wherein said decisions comprise aplurality of hard decisions and a plurality of soft decisions.
 6. Theapparatus according to claim 5, wherein (i) a first of saidprobabilities corresponds to one of said hard decisions, (ii) a secondof said probabilities corresponds to one of said soft decisions and(iii) said merging comprises adding said first probability and saidsecond probability.
 7. The apparatus according to claim 1, wherein saidsecond circuit is further configured to assign a plurality ofmultiplicities in a plurality of multiplicity matrices in response tosaid modified probability, wherein said algebraic soft-decisiontechnique is based on said multiplicity matrices.
 8. The apparatusaccording to claim 7, wherein (i) said second circuit is furtherconfigured to interpolate a bivariate polynomial in response to saidmultiplicity matrices and (ii) said algebraic soft-decision technique isbased on said bivariate polynomial.
 9. The apparatus according to claim8, wherein said second circuit is further configured to flip one or moreof said symbols in response to a constant term of said bivariatepolynomial being non-zero.
 10. The apparatus according to claim 1,wherein said apparatus is implemented as one or more integratedcircuits.
 11. A method of cyclic code decoding, comprising the steps of:(A) generating (i) a plurality of symbols and (ii) a plurality ofdecision values both in response to detecting an encoded codeword; (B)generating a plurality of probabilities to flip one or more of saidsymbols based on said decision values; (C) generating a modifiedprobability by merging two or more of said probabilities of anunreliable position in said symbols; and (D) generating a decodedcodeword by decoding said symbols using an algebraic soft-decisiontechnique in response to said modified probability.
 12. The methodaccording to claim 11, wherein (i) said probabilities are generatedusing a Chase technique and (ii) said algebraic soft-decision techniquecomprises a Koetter-Vardy technique.
 13. The method according to claim11, wherein said encoded codeword comprises one of (i) a Reed-Solomonencoded codeword and (ii) a BCH encoded codeword.
 14. The methodaccording to claim 11, wherein said detecting comprises a softdetecting.
 15. The method according to claim 11, wherein said decisionscomprise a plurality of hard decisions and a plurality of softdecisions.
 16. The method according to claim 15, wherein (i) a first ofsaid probabilities corresponds to one of said hard decisions, (ii) asecond of said probabilities corresponds to one of said soft decisionsand (iii) said merging comprises adding said first probability and saidsecond probability.
 17. The method according to claim 11, furthercomprising the step of: assigning a plurality of multiplicities in aplurality of multiplicity matrices in response to said modifiedprobability, wherein said algebraic soft-decision technique is based onsaid multiplicity matrices.
 18. The method according to claim 17,further comprising the step of: interpolating a bivariate polynomial inresponse to said multiplicity matrices, wherein said algebraicsoft-decision technique is based on said bivariate polynomial.
 19. Themethod according to claim 18, further comprising the step of: flippingone or more of said symbols in response to a constant term of saidbivariate polynomial being non-zero.
 20. An apparatus comprising: meansfor generating (i) a plurality of symbols and (ii) a plurality ofdecision values both in response to detecting an encoded codeword; meansfor generating a plurality of probabilities to flip one or more of saidsymbols based on said decision values; means for generating a modifiedprobability by merging two or more of said probabilities of anunreliable position in said symbols; and means for generating a decodedcodeword by decoding said symbols using an algebraic soft-decisiontechnique in response to said modified probability.